Risk field enhanced game theoretic model for interpretable and consistent lane-changing decision makings

<div class="section abstract"><div class="htmlview paragraph">This paper presents an integrated modeling approach for real-time discretionary lane-changing decisions by autonomous vehicles, aiming to achieve human-like behavior. The approach incorporates a two-player normal-form game and a novel risk field method. The normal-form game represents the strategic interactions among traffic participants. It captures the trade-offs between lane-changing benefits and risks based on vehicle motion states during a lane change. By continuously determining the Nash equilibrium of the game at each time step, the model decides when it is appropriate to change the lane. A novel risk field method is integrated with the game to model risks in the game pay-offs. The risk field introduces regions along the desired target lane with different time headway ranges and risk weights, capturing traffic participants' complex risk perceptions and considerations in lane-changing scenarios. It goes beyond simple gap acceptance assumptions used in previous studies, providing more human-like risk estimations. Discretionary lane-changing data from human drivers extracted from the NGSIM I80 dataset were employed to calibrate the integrated model for human-like lane-change decisions. The calibration results demonstrate the high prediction accuracy of the proposed model compared to previous studies. The calibrated risk field parameters in the model provide interpretability and contribute to a deeper understanding of human lane-changing decisions. The proposed model also exhibits improved consistency in lane-changing decisions within a continuous time range around the lane-crossing moment. It outperforms previous game-theoretic models that rely on acceleration and time pay-offs with specific assumptions about future vehicle motions. Several case studies were carried out in the co-simulations of CARLA and SUMO software and based on the NGSIM dataset samples. The model's ability to produce reliable and interpretable lane-changing decisions enhances autonomous vehicles' overall safety and user experience.</div></div>